ORGANIZERS AND SUPPORTERS

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2 ORGANIZERS AND SUPPORTERS Organized by Research Center for Informatics, Indonesia Institute of Sciences - Indonesia Supported by Research Center for Informatics, Indonesia Institute of Sciences - Indonesia IEEE Section Indonesia Ministry of Communication and Information - Kominfo iii

3 SCIENTIFIC AND ORGANIZING COMMITTEE Scientific Committee Ana Hadiana (Research Center for Informatics, Indonesian Institute of Sciences) - Chair Agus Subekti (Research Center for Informatics, Indonesian Institute of Sciences) - Co-Chair Roberto Togneri (Electrical, Electronic and Computer Engineering, The University of Western Australia, Australia) Bernadetta Kwintiana Ane ( Institute of Computer-aided Product Development Systems, Universitat Stuttgart, Germany) Mohd Yusoff Mashor (University of Malaysia Perlis, Malaysia) Md. Mahmud Hasan (Kazakh-British Technical University, Kazakhstan) Moncef Gabbouj (Tampere University of Technology, Finland) Anitawati Mohd Lokman (Universiti Teknologi MARA, Malaysia) Hilman Ferdinandus Pardede (Research Center for Informatics, Indonesian Institute of Sciences, Indonesia) Laksana Tri Handoko (Research Center for Informatics, Indonesian Institute of Sciences, Indonesia ) Andriyan Bayu Suksmono (School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia) Kuncoro Wastuwibowo (Chair IEEE Indonesia Section, Indonesia) Anto Satriyo Nugroho (Center for Information & Communication Technology Center for the Assessment & Application of Technology, BPPT, Indonesia) Estiko Rijanto (Research Center for Electrical Power and Mechatronics, Indonesian Institute of Sciences, Indonesia) John Batubara (School of Electrical Engineering, Bina Nusantara University, Indonesia) Chan Basaruddin (Faculty of Computer Science, Indonesia University, Indonesia) Rifki Sadikin (Research Center for Informatics, Indonesian Institute of Sciences, Indonesia) iv

4 2013 International Conference on Computer, Control, Informatics and Its Application - Frontmatters Contents [Plenary Speaker] Biological blind-tracking task through artificial Intelligence 1 Md. Mahmud Hasan [Plenary Speaker] KE AS AFFECTIVE DESIGN METHODOLOGY 7 Anitawati Mohd Lokman [Plenary Speaker] What do Machine Learning and Particle Swarm Optimization have to do with Content-Based Search in Large Multimedia Databases? 15 Moncef Gabbouj [Plenary Speaker] Remote Sensing: Searching Better Accuracy for Greenhouse Gasses Monitoring 17 Bernadetta Kwintiana Ane Fuzzy-Based Spectrum Handoff and Channel Selection for Cognitive Radio Networks 23 Ejaz Ahmed, Liu Yao, Salman Ali, Muhammad Shiraz and Abdullah Gani PEGAS: Partitioned GTS Allocation Scheme for IEEE Networks 29 M. Udin Harun Al Rasyid, Lee Bih-Hwang and Amang Sudarsono Goal Programming based Multi-criteria Decision-making for Distributed Denial of Service Attacks in Wireless Sensor Networks 33 Rolla Alomary and Salman Khan Proposed Analysis of Dust and Sand Storms Effects on Satellite Links in Saudi Arabia 39 Abdulaziz Alyami, Kamal Harb, Samir Abduljauwad, Omair Butt, Abdullah, and Amin Suharjono Performance Analysis of Coordinated Distributed Data Scheduling Schemes in Wireless Mesh Network 43 Dwi Rochma Agustiningsih, Nachwan Mufti Adriansyah and Tody Ariefianto Hop Distances Optimization for Balancing The Energy Consumption of Multi-hop Clustered Wireless Sensor Networks 49 Amin Suharjono, Wirawan Wirawan and Gamantyo Hendrantoro Identification of Orchid Species Using Content-Based Flower Image Retrieval 53 Diah Harnoni Apriyanti, Aniati Murni Arymurthy and Laksana Tri Handoko Semi Automatic Detector of Plasmodium Falciparum on Microscope Image Based 59 Iis Hamsir Ayub Wahab, Adhi Susanto, P. Insap Santosa and Maesadji Tjokronegoro Weaving Effects in Metamorphic Animation of Tree-like Fractal based on a Family of Multi-transitional Iterated Function System Code 65 Tedjo Darmanto, Iping Supriana Suwardi and Rinaldi Munir vi

5 Portable Smart Sorting and Grading Machine for Fruits Using Computer Vision 71 Hadha Afrisal, Muhammad Faris, Guntur Prasetyo, Lafiona Grezelda, Indah Soesanti and Mochammad Firdaus Paddy Diseases Identification with Texture Analysis using Fractal Descriptors Based on Fourier Spectrum 77 Auzi Asfarian, Yeni Herdiyeni and Aunu Rauf Hybrid K-Means Clustering With Region Growing Algorithm For Acute Leukemia Blood Cells Image Segmentation 83 Farah Hanim Abdul Jabar, Waidah Ismail, Rosalina Abdul Salam and Rosline Hassan Optimal Energy Control of DC Motor Speed Control: A Comparative Study 89 Hari Maghfiroh, Oyas Wahyunggoro and Adha Imam Cahyadi Control of a Magnetic Levitation System Using Feedback Linearization 95 Rudi Uswarman, Adha Imam Cahyadi and Oyas Wahyunggoro Stable Extended Predictive Control 99 Daniel Viudez-Moreiras, Angelo Raimondi, Isaias Martin and Juan Martin-Sanchez A Robust Feedback Gains for Linear Systems with Multiple Delay Components 105 Adha Cahyadi and Yoshio Yamamoto Auto-Tuning Quadcopter Using Loop Shaping 111 Hilton Tnunay, Muhammad Abdurrohman, Yuliyanto Nugroho, Reka Inovan, Adha Cahyadi and Yoshio Yamamoto A Comparative Study of PID, ANFIS and Hybrid PID-ANFIS Controllers for Speed Control of Brushless DC Motor Drive 117 Hidayat Tanjung, Sasongko Pramonohadi, Sarjiya and Suharyanto Adaptive Prefetching of Podcasts in a Limited Bandwidth Network 123 Windhya Hansinie Rankothge and Gihan Dias Performance Evaluation of Coarse Time Synchronization on OFDM under Multipath Channel 129 Suyoto, Agus Subekti and Sugihartono Application of Extremal Optimization Algorithm to Multi-objective Topology Design of Enterprise Networks 135 Salman Khan Design and implementation of an Internet of Things based Quality Control System 141 Yuhao Deng, Haiping Zhu, Guojun Zhang and Hui Yin Delta Encoding Based Data Compression for Coastal Radar 147 Nuryani and Hendrawan Comparison Proactive and Reactive Routing Protocol in Mobile Adhoc Network Based on Ant Algorithm 153 Istikmal, Leanna Vidya Yovita and Basuki Rahmat Three Dimensional Data Clustering and Visualization Using 3D-Self Organizing Maps159 Doddy Tri Hutomo and Yeni Herdiyeni vii

6 Whitefly (Bemisia tabaci) Calculation Based on Image Processing using Triangle Method 165 Doddy Tri Hutomo and Yeni Herdiyeni Application Image Processing to Predict Personality Based on Structure of Handwriting and Signature 169 Esmeralda Contessa Djamal, Sheldy Nur Ramdlan and Risna Darmawati Sugarcane Leaf Color Classification in Sa*b* Color Element Composition 175 Hari Ginardi, Riyanarto Sarno and Tri Adhi Wijaya Spline and Color Representation for Batik Design Modification 179 Nanik Suciati, Anny Yuniarti, Chastine Fatichah and Rizky Januar Akbar An improvement technique of fragile watermarking to assurance the data integrity on vector maps 185 Shelvie Nidya Neyman, Fransisca Cahyono and Benhard Sitohang A Lithium-ion Battery Modeling For A HIL-Battery Simulator 191 Grace Pebriyanti A Comprehensive Characterization of A Linear Deformation Sensor for Applications in Triaxial Compression Tests 197 Riska Ekawita, Khairurrijal Khairurrijal, Hasbullah Nawir, Suprijadi Suprijadi and Muhammad M. Munir Design Mini SCADA For Furnace Induction Reactor of Kernel Coating 201 Adi Abimanyu, Sukarman Sukarman, Gina Kusuma, Jumari Jumari, Triyono Triyono and Dwi Yuliansari Nurazizah Breaking Through Memory Limitation in GPU Parallel Processing Using Strassen Algorithm 207 Pujianto Yugopuspito, Sutrino Cahya and Robertus Hudi Experimental Study of DC Motor Control via Communication Network using Markovian Jump System Approach 213 Indra Sakti, Bambang Riyanto Trilaksono and Asep Najmurrokhman Comparison the Insertion Attack Effects on Randomness Property of Dragon and Rabbit Stream Cipher 219 Desi Wulandari, Aries Kumala, Setyo Nugroho and Santi Indarjani Implementation of Insertion Attack on Pseudorandom Number Generator ANSI X9.17 and ANSI X9.31 based on Statistical Distance Tests and Entropy Difference Tests 225 Kuni Inayah, Bondan Estuwira Sukmono, Rahmat Purwoko and Santi Indarjani Supporting Decision Making in Situational Crime Situation using Fuzzy Association Rule 231 Noor Maizura Mohamad Noor, Wan Mohd Farhan Wan Nawawi and Ahmad Faiz Ghazali Dengue Notification System using Fuzzy Logic 237 Rajul Rosli Razak, Rosmawati Abd. Wahab, and Muhammad Hermi Ramli Linear-Quadratic Cost Function for Dynamic System Modelling Using Recurrent Neural Networks 243 viii

7 Erwin Sitompul Commodity Price Prediction Using Neural Network, Case Study: Crude Palm Oil Price 249 Robert Gunawan, Masayu Leylia Khodra and Harlili A Web-Based IT Asset Management Application Using Fuzzy Logic in Vendor Selection Process 255 Valencia Wijaya, Ririn Ikana Desanti and Samuel Lukas Linear Function and Inverse Function with Weight Ratio for Improving Learning Speed of Multi-layer Perceptrons Feed-forward Neural Networks 261 Dian Andriana Application Distribution Model In Volunteer Computing Environment Using Peerto-Peer Torrent Like Approach 267 Yustinus Soelistio Weighted Ontology and Weighted Tree Similarity Algorithm for Diagnosing Diabetes Mellitus 273 Sugiyanto, Widhy Hayuhardhika Nugraha Putra, Riyanarto Sarno and Mohamad Sidiq An Empirical Study of Unethical Behavior in a Tertiary Institution in Malaysia 279 Azhar Abd Aziz, Arwin Idham Mohamad, Anitawati Mohd Lokman and Zawiyah Mohammad Yusof Data Center for Integrating Weather Monitoring Systems 283 Purnomo Husnul Khotimah and Devi Munandar Indonesian Hadith Retrieval System using Thesaurus 289 Ikhsan Rasyidi, Ade Romadhony and Agung Toto Wibowo Predict Fault-Prone Classes using the Complexity of UML Class Diagram 293 Arwin Halim Text Message Categorization of Collaborative Learning Skills in Online Discussion Using Support Vector Machine 299 Erlin, Unang Rio and Rahmiati PRODML Performance Evaluation as SOT Data Exchange Standard 305 Bobby Suryajaya and Charles Lim Support Vector Regression for Service Level Agreement Violation Prediction 311 Ahmad Fadzil M Hani, Irving Vitra Paputungan, and M Fadzil Hassan A Collaborative Portal for Integrating Resources in Grids 317 Al Farisi and Irving Paputungan Clustering of ERP Business Process Fragments 323 Riyanarto Sarno, Hari Ginardi, Endang Wahyu Pamungkas and Dwi Sunaryono Integration of Genetic And Tabu Search Algorithm Based Load Balancing for Heterogenous Grid Computing 329 Irfan Darmawan, Kuspriyanto, Yoga Priyan and Ian Joseph M ix

8 Multi Agents based Traditional Market Customers Behavior Design 335 Purba Daru Kusuma and Azhari Sn Decision Mining for Multi Choice Workflow Patterns 341 Riyanarto Sarno, Putu Linda I. Sari, Hari Ginardi, Dwi Sunaryono and Imam Mukhlash Accelerating Genetic Schema Processing Through Local Search 347 Tarek El-Mihoub, Adrian Hopgood and Ibrahim Aref

9 2013 International Conference on Computer, Control, Informatics and Its Application Semi Automatic Detector of Plasmodium Falciparum on Microscope Image Based Iis Hamsir Ayub Wahab Jurusan Teknik Elektro dan Teknologi Informasi Universitas Gadjah Mada Yogyakarta, Indonesia Adhi Susanto Jurusan Teknik Elektro dan Teknologi Informasi Universitas Gadjah Mada Yogyakarta, Indonesia Paulus Insap Santosa Jurusan Teknik Elektro dan Teknologi Informasi Universitas Gadjah Mada Yogyakarta, Indonesia Maesadji Tjokronegoro Fakultas Kedokteran Universitas Gadjah Mada Yogyakarta, Indonesia Abstract The inspection standard for diagnosis of active malaria for parasites in blood slides through a microscope. Although microscopy has good sensitivity and allows species identification and parasite counts it requires microscopy expertise and involves a labor intensive repetitive task which is time consuming. The main aim of this research is to build an automatic system for identify and classify the parasite plasmodium falciparum based on a digital image scheme. The results of this research show the color features can give higher accuracies based on the architecture of LVQ network with four hidden neurons. Keywords computer aid; detection; plasmodium falciparum; identify; classication I. INTRODUCTION Standard definitive diagnosis of malaria infection is to find parasites in blood slides through a microscope. However, it is a routine and time-consuming task. In addition, recent studies in the field suggest that the level of agreement among clinical experts for diagnosis is quite low [1]. Therefore, it is essential to generate neither the tools nor the general standard algorithm that is able to diagnose. Automatic detection of malaria parasites have been made by some researchers that the number of malaria parasites in the blood can be calculated based on the acquisition of blood samples were converted into digital images. Detection systems are generally built through several processing stages, namely: image acquisition, image pre-processing, image segmentation, feature extraction and classification. Selena, et al. [2], develop an automated system for counting malaria parasites quickly and accurately based image analysis. The image obtained from the acquisition using a video camera 3-CDD. There are four steps being taken to be able to count the number of parasitemia, namely: edge detection, edge linking, clump splitting and parasite detection. Parasites can be identified based on the characteristic properties of the edge of the red blood cells by using Sobel operator and the transformation Hough [3]. This is also done by Diaz et al. [4], but the system is built using a low pass filter on the preprocessing of the image, and then extracts the image preprocessing results in order to obtain information which the blood cells infected with malaria parasites. Detection of malaria parasite can be improved when using machine learning strategies [5]. The system is a system built classifiers based on feature of each type of parasite. Features that have been extracted from previous experiments are feature histogram [6][7], morphological [8], texture [9]. The classifier system is used multilayer perceptron neural network (MLP) [5], support vector machine (SVM) [4][10], k-nn [3], and the type of back propagation[3]. The main objective of this research is to design a computerassisted system for detecting and classifying parasite Plasmodium falciparum in the blood preparations are indicated based digital microscopic images using learning vector quantization neural networks and image segmentation. Analysis of the methods tested is the detection and classification accuracy, the speed of the system is built. For this study, we also consider the developmental cycle of Plasmodium falciparum, i.e. gametocyte, ring, trophozoite and schizonts. II. MATERIAL AND METHOD A. Material In this study, the data samples used were obtained from blood samples of patients on glass microscope slides. Then preparations in the form of a digital image file in Jpg format with 24-bit color depth and size of 256x256 pixels. The number of data is 96 with the number of class 4, according to the type of variation in the condition of Plasmodium falciparum. B. Method The proposed method can be seen in Figure 1. This method consists of: pre-processing of the image based on the following techniques are discussed i.e. the process of image filtering, segmentation, feature extraction and classification. 59

10 Fig. 1. The proposed method Here's an explanation of the stages of the overall system: 1) Image aqcuisition: this process aims to obtain images of the preparations plasmodium falciparum which will be further processed as an object to be classified. 2) Pre-proccesing: image pre-processing is done to improve the quality of the image obtained by the change of optical to electrical likely caused a decline in the quality of the sensor noise, blur due to camera or a lack of focus because the camera relative movement of the object. Image enhancement techniques used in this study is the bilateral filter. Bilateral filter is an improved method of image quality of nonlinear adaptive. This method is a combination of two filter methods, namely domain filter and range filter [13]. Combined the two enables a more effective improvement to the quality of the image is degraded by noise. Bilateral filter method used two kinds of weighting the pixels in the image, thus resulting in a more accurate calculation than previous methods [12]. The first weight in the bilateral filter is a method of spatial weights, which calculates the geometric proximity of pixels, while the second weight is the weight that measures the photometric color intensity differences between pixels in the image. Both of these weight calculations will result in effective weight values for proximity into account the distance between pixels and color intensity differences between pixels. Domain low pass filter used in image g(x) to produce the output image is defined as follows. Where c( x) ( ξ ) c( ξ x) = k ( x g dξ 1 f ( x) d ) (1) ξ is a closed geometric measure between the center of neighbors x and the point of the proximityξ, f and g are both factually a multiband image input and output. To maintain a low pass component of the signal, then: ( ) k d ( x) = c ξ dξ (2) if the shift invariant filter, then the c( x) ξ is simply a different function of vector ξ - x, and k d is constants. The same manner filter range obtained using the following equation. 1 f ( x) = kr ( x) g( ξ ) s( f ( ξ ) g( x) ) dξ (3) In this case, the kernel will measure photometric similar between the pixels. Normalization constant in this case is ( ξ ) k r ( x) = s( f g( x)) dξ (4) Spatial distribution of image intensity is not so instrumental to filter the range by himself. The combination of the intensity of the whole image would make a little sense of change, because the distribution of image values far away from the x and otherwise not to affect the final value at x [13]. Additionally, it can be shown that the filter without the filter range domain is just changing the color of an image map. Appropriate solution is to combine domain filter and range, thus strengthening the geometric and photometric aspects. Combination filter range and domain filter can be shown in the following equation: 1 f ( x) = kd ( x) g( ξ ) c( ξ x) s( g( ξ ) g( x) ) dξ (5) With normalization: ( ξ x) s( g( ξ ) g( x ) k ( x) = c ) dξ (6) Combination filter range and domain filter is called the bilateral filter. The image quality has been improved, and then will be separation of objects based on color intensity. This is because the objects which will split the intensity of color tend to have different and each object has a nearly uniform color [6]. In this study, k-mean segmentation method is used to separate objects. K-mean method is basically to divide the image histogram. The stages are as follows. - Find the maximum and minimum intensity in the image - From minimum to maximum intensity do the division number N. N determines the number of objects that are expected to exist in the image. - After the distribution, the histogram will be divided into sections called clusters. Then do a search for the image of the whole point, every point will be assigned to the nearest cluster so that the end result of this process is the number of colors in the image into N. - Then look for the results rata-rata/mean whole point in each cluster, - Then change the color of all the points in the clusters is the average of each cluster. 3) Feature extraction: to obtain the specific characteristics of each image acquired malaria parasite preparations and give each pattern in the image. To extract the characteristics of each image can be used characterize the color histogram 4) Classification: in this study, the classification method used is a neural network learning vector quantization (LVQ). LVQ is a method to conduct the training competitive layers are supervised learning [14]-[16]. LVQ consists of two layers, namely layers competitive and linear layers as shown in Figure 2. 60

11 R1 Gi Bn input w1o wi1 wn1 w11 w1m wij wim wio w1j wnj wnm wno c1 cj cm co vj1 vjn vm1 vo1 hidden neuron vmn vjk vjl vmk vml von vok vol Fig. 2. LVQNN Architecture v11 v1k v1l v1n T1 Tk Tl Tn output The first layer is a competitive layer classifies the input with a competitive manner. The second layer is linear layer transforms the sub-classes to be output first tier classes that have been defined previously the target. Each subclass is represented by a single neuron in the output layer are competitive and each class is represented by a linear neuron in the output layer. Output neurons in the competitive layer is usually referred to as the hidden neurons and linear neurons in the output layer is referred to as the output neuron. Subclass the competitive layer is the layer that competition results in the classroom while the linear layer is defined by the user class. Processing that occurs at each neuron is finding the shortest distance between an input vector to the corresponding weight. In this case w is the weight vector connecting each neuron in the input layer to the hidden neurons in the output layer. Activation function in the output layer will map the weight vector of the linear layer to the output layer. From Figure 2 it can be seen in the competitive layer neuron is connected to every input neuron to the hidden certain weight (w). Whereas in each hidden layer neuron is connected linearly with the output neurons with a certain weight (v). The output of the plasmodium falciparum gametocyte be categorized in class 1, the plasmodium falciparum ring will be categorized in class 2, the plasmodium falciparum schizonts will be categorized in classes 3 and the plasmodium falciparum trophozoite be categorized in class 4. The test results will be used to determine the type of parasite detection. Here are given training algorithm LVQ network if the data is given by m n input variables. LVQ network training algorithms: Set: Initial weight of the j-th input variable to the class heading into the Wij-i, with i = 1, 2,..., K and j = 1, 2,..., m. Maximum epoch = Maxepoh. Parameter learning rate = α. Reduction in learning rate = Dec. α. Minimal learning rate allowed α = Min. Enter: Data input Xij with i = 1, 2,..., n and j = 1, 2,..., m. Targets in the form of classes, ie Tk with k = 1, 2,..., n. Set initial conditions epoch, the epoch = 0. Do if (epoch Maxepoh) and ( α α Min) epoch = epoch 1 Done for i = 1 to n Define J such that Xi - Wj j = minimum with 1, 2,, K. Fix Wj with the following provisions: If T = Cj then Wj = Wj α (Xi - Wj) If T Cj then Wj = Wj - α (Xi - Wj) Subtract the value of α (α value reduction can be done with α = α - Dec. α or α = α way * Dec. α. end After the training will be obtained final weights (W). A weight is what will be used to perform testing or simulation with different data (not the same as the data used for training). LVQ network testing algorithm: Enter the data to be tested, for example, Xij with i = 1, 2,..., np and j = 1, 2,..., m. Done for i = 1 to np Define J such that Xi - Wj j = minimum with 1, 2,..., K. J is the class for Xi The parameters used in the LVQ neural network in this study: the rate of initial training α = 0.01; decrease in the rate of training Dec. α = 0.1; minimum training rate Min α = 10-6 ; maximum epoch = 5000;, and Hidden neurons = 4. The terms used to stop training are the convergence of the weight vector value to a fixed value. III. RESULT The resulting digital image of the acquisition process in this study still have lighting effects, focusing on the optical lens and the camera, and the effect of noise so the recovery should be done by using the bilateral filter. In the screening process, first color space is converted to the CIELAB color space. This is done because the results of image filtering in CIELAB color space using the bilateral filter will produce natural colors images [13]. The results of this filtering will be reduce the noise on the image so that the image of the color components red, blue and green for all images undergo significant changes. The best results from the use of domain filter parameter equal to 3 pixels together with the results of experiments carried out [6], [12] and [13] which uses the same values to calculate the weight of proximity pixels geometrically, while the weight of the photometric measures the difference between the color intensity pixel in the image is what changed the value to get the best results. Three dimensional visualization of the image before and after filtered using a bilateral filter can be seen in Figure 3. After the restoration of the image of the sample preparation, the next step is to separate the parts of blood parasites on image segmentation using k-mean. In this study, the segmentation process performed prior image color space conversion from RGB color space to CIELAB color. Grouping colors in CIELAB color space is obtained by using the Euclidean distance formula of elements of L *, a *, and b *, and thus will get the average color values corresponding closest 61

12 distance of each color. K values used in the simulation are k = 3, 4, 5, 6, and 7. Segmentation results for sample preparation for each of plasmodium falciparum can be seen in Figure 4. (a) (b) Fig. 3. 3D modeling for image (a) before and (b) after filtered (a) (b) Separation of objects for plasmodium falciparum gametocyte and schizonts of red blood cells can be done either when the value of k = 3. Separation can be done well because of the morphological form of the two types of plasmodium is very clear where plasmodium falciparum gametocyte shaped like a banana and plasmodium falciparum schizonts shaped blobs. For a sample image of plasmodium falciparum ring, separation of objects with red blood cells can be done with the best value of k = 3 because of the morphological shape flaciparum plasmodium is shaped like a ring that is sometimes very thin. In the experiments that have been done by adding the value of k in the sample group, the object will come with reduced color other objects so that the object will be damaged or lost when given a value of k greater than 3 [4][5]. As with the plasmodium falciparum trophozoite, to separate the object from the red blood cells required divider value different k is 3, 5 and 7. The output of the segmentation is then taken as the peak color component characteristic vector input pattern classification plasmodium falciparum. An image of the object has a specific color. To state the color characteristics of the image of the object to note how the behavior of the distribution of the color itself. Granted many image objects have the same basic color but the color distribution in it have turned out differently[4]. Distribution can be obtained using the color histogram of colors. In this study, the peak value of the feature of color histogram is used as input to the classification system. Results of the peak value of the RGB color components can be seen in Table 1. After the feature extraction process of the sample image, the next step is to use the maximum color component feature of as input vector pattern classification parasites using LVQ neural network. LVQ is designed to classify four types of plasmodium falciparum parasite image so that the network will have four target encoding pattern with the number of data samples of 96 copies. Prior to conducting experiments training and testing system, initial weights are determined in advance. Initial weights are determined from the average value across the data for each of the color feature shown in Table 2. Experiment is to observe the performance of the network by varying the rate of training. LVQ will be tested with the training given the rate of α of 0.01; 0.02; 0.03; 0.04; 0.05, and 0.1. Table 3 showed that the accuracy of training data and test data for all the same training the rate of, i.e. 100% of the trained image data 96 and 91.67% of the 48 new data were tested on this network. Reaching network to converge at epoch 66 with MSE values approaching fault tolerance value that is equal to 1, (Figure 5). This suggests that the rate of addition of training on LVQ network with input feature do not affect the color of the center point of the neurons, so that the training data is not moved to another class [6]. So the network has a steady state for input color feature. (c) (d) Fig. 4. Results of image segmentation plasmodium falciparum (a) gametocyte, (b) ring (c) schizonts and (d) trophozoite 62

13 TABLE I. RANGE OF COLOR FEATURE FOR FALCIPARUM PLASMODIUM Sample of Plasmodium falciparum Rmaks Gmaks Bmaks gametocyte ring schizonts trophozoite TABLE II. CODE OF TARGET PATTERN OF NEURAL NETWORK AND THE INITIAL WEIGHT RANGE OF COLOR FEATURE initial weight of the Sample of Plasmodium Bit output of neuron color feature falciparum Rmax Gmax Bmax gametocyte ring schizonts trophozoite Training-Blue Goal-Black TABLE III. 9 x 10-3 Performance is 9.55e-006 Goal is 1e Epoch training rate Fig. 5. Performance of training LVQ EFFECT OF THE TRAINING RATE OF COLOR FEATURE MSE training data Accuracy data test 0,01 9, % 91,67% 0,02 9, % 91,67% 0,03 9, % 91,67% 0,04 9, % 91,67% 0,05 9, % 91,67% 0,1 9, % 91,67% I. CONCLUSION REFERENCES [1] K. Mitiku, G. Mengistu, and B. Gelaw. The reliability of blood film examination for malaria at the peripheral health unit. Ethiop.J.Health Dev, 17(3):1pp , [2] Selena W.S. Sio, Weiling Sun, Saravana Kumar, Wong Zeng Bin, Soon Shan Tan, Sim Heng Ong, Haruhisa Kikuchi, Yoshiteru Oshima, Kevin S.W. Tan. MalariaCount: An image analysis-based program for the accurate determination of parasitemia. Elsevier [3] Minh-Tam Le, Timo R Bretschneider, Claudia Kuss and Peter R Preiser. A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears. BMC Cell Biology 2008, 9:15 doi: / [4] Gloria Díaz, Fabio A. González, Eduardo Romero. A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Journal of Biomedical Informatics 42 (2009) [5] Charles Ma, Paul Harrison, Lina Wang, Ross L Coppel. Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears. Malaria Journal, 9: [6] Wahab, Iis Hamsir Ayub. Identifikasi Parasit Malaria Dalam Darah Menggunakan Metode Segmentasi Citra Digital Dan Jaringan Syaraf Tiruan. Tesis. Teknik Elektro. Universitas Gadjah Mada [7] Wahab, Iis H A., Adhi Susanto and Litasari. Classification of Plasmodium falciparum Using Learning vector quantization Neural Network. Proceeding of International Conference on Biomedical Engineering, pp 17. Surabaya, Indonesia [8] Subhamoy Mandal, Amit Kumar, J Chatterjee, Manjunatha and Ajoy K. Segmentation of Blood Smear Images using Normalized Cuts for Detection of Malarial Parasites. Annual IEEE India Conference (INDICON) / [9] Vishnu V. Makkapati, Reghuveer M. Rao. Segmentation of Malaria Parasites in Pheriperal Blood Smear Images. ICASSP / [10] F Boray Tek, Andrew G Dempster and Izzet Kale. Computer vision for microscopy diagnosis of malaria. Malaria Journal [11] Dian Anggraini, Anto Satriyo Nugroho, Christian Pratama, Ismail Ekoprayitno Rozi, Aulia Arif Iskandar5, Reggio Nurtanio Hartono. Automated Status Identification of Microscopic Images Obtained from Malaria Thin Blood Smears. International Conference on Electrical Engineering and Informatics. Bandung, Indonesia [12] Adrianto, Hari Agung., Fahren Bukhari., dan Laura Prescilla. Penggunaan Kualitas Citra Warna Menggunakan Metode Bilateral Filter. Jurnal Departemen Ilmu Komputer Vol. 5 - No. 2. Bogor Agricultural University [13] Tomasi, C and Manduchi, R. Bilateral Filtering for Gray and Color Images. Proceedings of the IEEE International Conference on Computer Vision. Bombay, India [14] J. C. Bezdek and N. R. Pal. Two soft relatives of learning vector quantization. IEEE Trans. Neural Networks, vol. 8, no. 5, pp [15] Kohonen T. New developments of learing vector quantization and the self-organizing map. Symposium on Neural Networks; Alliances and Perspectives in Senri 1992 (SYNAPSE' 92), Osaka, JAPAN [16] Kohonen, T. Self-organizing maps, Springer, Berlin, Bibliography on the Self-Organizing Map (SOM) and Learning vector quantization (LVQ). Neural Networks Research Centre, Helsinki University of Technology The analytical results of the tests performed on the system identification of plasmodium falciparum is known that the use of bilateral filter, segmentation using k-mean clustering method and LVQ neural network with feature color as vectors enter it was able to identify the types of parasite plasmodium falciparum well. 63